from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase          =						{
    """configuration_conditional_detr""": [
        """CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConditionalDetrConfig""",
        """ConditionalDetrOnnxConfig""",
    ]
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase          =						["""ConditionalDetrFeatureExtractor"""]
	_UpperCAmelCase          =						["""ConditionalDetrImageProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase          =						[
	    """CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """ConditionalDetrForObjectDetection""",
	    """ConditionalDetrForSegmentation""",
	    """ConditionalDetrModel""",
	    """ConditionalDetrPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_conditional_detr import (
	    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    ConditionalDetrConfig,
	    ConditionalDetrOnnxConfig,
	)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
		from .image_processing_conditional_detr import ConditionalDetrImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ConditionalDetrForObjectDetection,
		    ConditionalDetrForSegmentation,
		    ConditionalDetrModel,
		    ConditionalDetrPreTrainedModel,
		)

else:
	import sys

	_UpperCAmelCase          =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







import numpy as np
from PIL import Image




def 							UpperCamelCase		(			__lowercase		:					np.ndarray		,__lowercase		:					int		,__lowercase		:					int      ):
		'''simple docstring'''




		A_						:							List[Any]      = np.array(__lowercase      )
		if arr.shape[0] != arr.shape[1]:
				raise ValueError('The input array is not a square matrix'      )
		A_						:							str      = 0
		A_						:							Any      = 0
		A_						:							Optional[int]      = 0
		A_						:							Optional[Any]      = 0

		# compute the shape of the output matrix
		A_						:							str      = (arr.shape[0] - size) // stride + 1
		# initialize the output matrix with zeros of shape maxpool_shape
		A_						:							str      = np.zeros((maxpool_shape, maxpool_shape)      )

		while i < arr.shape[0]:
				if i + size > arr.shape[0]:
						# if the end of the matrix is reached, break
						break
				while j < arr.shape[1]:
						# if the end of the matrix is reached, break
						if j + size > arr.shape[1]:
								break
						# compute the maximum of the pooling matrix
						A_						:							Any      = np.max(arr[i : i + size, j : j + size]      )
						# shift the pooling matrix by stride of column pixels
						j += stride
						mat_j += 1

				# shift the pooling matrix by stride of row pixels
				i += stride
				mat_i += 1

				# reset the column index to 0
				A_						:							Tuple      = 0
				A_						:							Optional[int]      = 0

		return updated_arr




def 							UpperCamelCase		(			__lowercase		:					np.ndarray		,__lowercase		:					int		,__lowercase		:					int      ):
		'''simple docstring'''




		A_						:							int      = np.array(__lowercase      )
		if arr.shape[0] != arr.shape[1]:
				raise ValueError('The input array is not a square matrix'      )
		A_						:							Any      = 0
		A_						:							Optional[int]      = 0
		A_						:							List[str]      = 0
		A_						:							Union[str, Any]      = 0

		# compute the shape of the output matrix
		A_						:							int      = (arr.shape[0] - size) // stride + 1
		# initialize the output matrix with zeros of shape avgpool_shape
		A_						:							int      = np.zeros((avgpool_shape, avgpool_shape)      )

		while i < arr.shape[0]:
				# if the end of the matrix is reached, break
				if i + size > arr.shape[0]:
						break
				while j < arr.shape[1]:
						# if the end of the matrix is reached, break
						if j + size > arr.shape[1]:
								break
						# compute the average of the pooling matrix
						A_						:							str      = int(np.average(arr[i : i + size, j : j + size]      )      )
						# shift the pooling matrix by stride of column pixels
						j += stride
						mat_j += 1

				# shift the pooling matrix by stride of row pixels
				i += stride
				mat_i += 1
				# reset the column index to 0
				A_						:							List[Any]      = 0
				A_						:							int      = 0

		return updated_arr


# Main Function
if __name__ == "__main__":
	from doctest import testmod

	testmod(name="""avgpooling""", verbose=True)

	# Loading the image
	_UpperCAmelCase          =						Image.open("""path_to_image""")

	# Converting the image to numpy array and maxpooling, displaying the result
	# Ensure that the image is a square matrix

	Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

	# Converting the image to numpy array and averagepooling, displaying the result
	# Ensure that the image is a square matrix

	Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()






